🤖 AI Summary
This study addresses the challenge of automating the processing of structured documents—such as government forms, medical records, and corporate invoices—in real-world scenarios where form layouts exhibit high variability. To this end, the authors introduce a multi-domain benchmark dataset comprising 407 real-world forms and present the first systematic evaluation of both traditional OpenCV-based methods and a suite of YOLO models—including YOLOv8, YOLOv11, YOLOv26-s, and YOLOv26-l—for the task of detecting fillable elements. Experimental results demonstrate that YOLOv11 consistently achieves the highest F1 scores and Jaccard accuracy across all element categories and tolerance levels, confirming its superiority in form understanding tasks. The released dataset and comprehensive performance comparison establish a valuable benchmark for future research in document intelligence.
📝 Abstract
Automated processing of structured documents such as government forms, healthcare records, and enterprise invoices remains a persistent challenge due to the high degree of layout variability encountered in real-world settings. This paper introduces AutoFormBench, a benchmark dataset of 407 annotated real-world forms spanning government, healthcare, and enterprise domains, designed to train and evaluate form element detection models. We present a systematic comparison of classical OpenCV approaches and four YOLO architectures (YOLOv8, YOLOv11, YOLOv26-s, and YOLOv26-l) for localizing and classifying fillable form elements. specifically checkboxes, input lines, and text boxes across diverse PDF document types. YOLOv11 demonstrates consistently superior performance in both F1 score and Jaccard accuracy across all element classes and tolerance levels.